



import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class   snake_case__							(_lowerCAmelCase   ,      unittest.TestCase						):







						"""simple docstring"""




						__lowerCAmelCase	:List[str]         =	FunnelTokenizer
						__lowerCAmelCase	:Union[str, Any]         =	FunnelTokenizerFast
						__lowerCAmelCase	:List[str]         =	True
						__lowerCAmelCase	:Any         =	True

						def 				SCREAMING_SNAKE_CASE__(  self    )   -> Union[str, Any]:
									"""simple docstring"""




									super().setUp()

									a__  :		Optional[int]					=    [
									    """<unk>""",
									    """<cls>""",
									    """<sep>""",
									    """want""",
									    """##want""",
									    """##ed""",
									    """wa""",
									    """un""",
									    """runn""",
									    """##ing""",
									    """,""",
									    """low""",
									    """lowest""",
									]
									a__  :		str					=    os.path.join(self.tmpdirname   ,  VOCAB_FILES_NAMES["""vocab_file"""]    )
									with open(self.vocab_file   ,  """w"""   ,  encoding="""utf-8"""    ) as vocab_writer:
												vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]    )    )

						def 				SCREAMING_SNAKE_CASE__(  self   ,  **__lowercase    )   -> List[Any]:
									"""simple docstring"""




									return FunnelTokenizer.from_pretrained(self.tmpdirname   ,  **SCREAMING_SNAKE_CASE_    )

						def 				SCREAMING_SNAKE_CASE__(  self   ,  **__lowercase    )   -> Union[str, Any]:
									"""simple docstring"""




									return FunnelTokenizerFast.from_pretrained(self.tmpdirname   ,  **SCREAMING_SNAKE_CASE_    )

						def 				SCREAMING_SNAKE_CASE__(  self   ,  __lowercase    )   -> Optional[Any]:
									"""simple docstring"""




									a__  :		str					=    """UNwant\u00E9d,running"""
									a__  :		Optional[int]					=    """unwanted, running"""
									return input_text, output_text

						def 				SCREAMING_SNAKE_CASE__(  self    )   -> Tuple:
									"""simple docstring"""




									a__  :		Union[str, Any]					=    self.tokenizer_class(self.vocab_file    )

									a__  :		Optional[int]					=    tokenizer.tokenize("""UNwant\u00E9d,running"""    )
									self.assertListEqual(SCREAMING_SNAKE_CASE_   ,  ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]    )
									self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_    )   ,  [7, 4, 5, 1_0, 8, 9]    )

						def 				SCREAMING_SNAKE_CASE__(  self    )   -> str:
									"""simple docstring"""




									a__  :		List[Any]					=    self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE_    )
									for tokenizer in tokenizers:
												a__  :		int					=    tokenizer("""UNwant\u00E9d,running"""    )
												a__  :		Union[str, Any]					=    len(inputs["""input_ids"""]    ) - 1
												self.assertListEqual(inputs["""token_type_ids"""]   ,  [2] + [0] * sentence_len    )

												a__  :		int					=    tokenizer("""UNwant\u00E9d,running"""   ,  """UNwant\u00E9d,running"""    )
												self.assertListEqual(inputs["""token_type_ids"""]   ,  [2] + [0] * sentence_len + [1] * sentence_len    )









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a      :  Optional[Any]			     =			{
    'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
    'tokenization_mvp': ['MvpTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  str			     =			['MvpTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  Optional[Any]			     =			[
	    'MVP_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'MvpForCausalLM',
	    'MvpForConditionalGeneration',
	    'MvpForQuestionAnswering',
	    'MvpForSequenceClassification',
	    'MvpModel',
	    'MvpPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
	from .tokenization_mvp import MvpTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mvp_fast import MvpTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mvp import (
		    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MvpForCausalLM,
		    MvpForConditionalGeneration,
		    MvpForQuestionAnswering,
		    MvpForSequenceClassification,
		    MvpModel,
		    MvpPreTrainedModel,
		)

else:
	import sys

	a      :  Optional[Any]			     =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






